The motive of our work is to achieve aspect angle and motion independent robust classification of relevant objects in inverse synthetic aperture radar imagery. It is required that the classification decision should incorporate an estimate of confidence in order to reject weak decisions due to critical aspect angles or unknown objects. The proposed architecture employs a cascaded combination of an unsupervised and a supervised trained neural network. The unsupervised trained Self-Organizing Feature Map is used for object segmentation by clustering a 2D feature space and the supervised multi-layer perceptron (MLP) classifier performs the object recognition based on extracted features from the segmented object. Various features characterizing the geometrical appearance and the scatterer distribution of the objects are investigated and a combination of features, which maximize the MLP classification rate, is selected. For comparison purposes a nearest neighbor classification approach is also considered. A grouping mechanism which groups together similar views of the object, reduces the degrees of freedom of the classification process by using its own adapted classifier for each group. On simulated noisy images a recognition rates over 90% for 10 different object classes has been achieved.